main.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive') # Mount to the main Google Drive directory"
      ],
      "metadata": {
        "id": "7g4XO-UpQkt8",
        "outputId": "929fa432-e8f3-4cae-bd14-d2377e71f662",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "zwFnJsE6vjf8"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install and Import Dependencies"
      ],
      "metadata": {
        "id": "KnEkhrKz2IMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import math\n",
        "import shutil         # used for performing high level operations on files\n",
        "import glob          # used to return all file paths that match a specific pattern"
      ],
      "metadata": {
        "id": "POB07nZ6O1SE"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# count the number of images in different classes\n",
        "root_dir = \"/content/gdrive/MyDrive/Brain_Tumor_Prediction/Dataset\"\n",
        "no_of_images = {}\n",
        "\n",
        "for dir in os.listdir(root_dir):\n",
        "  no_of_images[dir] = len(os.listdir(os.path.join(root_dir, dir)))\n",
        "\n",
        "no_of_images.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GscrfF3O9JE",
        "outputId": "55bc957d-c212-4348-efda-18e1cbdc1407"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('no', 737), ('yes', 537)])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dataFolder(p, split):\n",
        "  if not os.path.exists(\"/content/gdrive/MyDrive/Brain_Tumor_Prediction/\"+p):\n",
        "    os.mkdir(\"/content/gdrive/MyDrive/Brain_Tumor_Prediction/\"+p)\n",
        "\n",
        "    for dir in os.listdir(root_dir):\n",
        "      os.makedirs(\"/content/gdrive/MyDrive/Brain_Tumor_Prediction\"+p+\"/\" +dir)\n",
        "\n",
        "      for img in np.random.choice(a = os.listdir(os.path.join(root_dir,dir)),\n",
        "                                  size = (math.floor(split*no_of_images[dir])-5),\n",
        "                                  replace = False):\n",
        "        O = os.path.join(root_dir,dir,img)\n",
        "        D = os.path.join(\"/content/gdrive/MyDrive/Brain_Tumor_Prediction/\"+p, dir)\n",
        "        shutil.copy(O,D)\n",
        "        os.remove(O)\n",
        "  else:\n",
        "    print( f\"{p} Folder already exists!\")"
      ],
      "metadata": {
        "id": "jQteszpcPjX8"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataFolder(\"Train\",0.7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CTttXNFRmy7",
        "outputId": "8e9d723b-e275-4bcc-cb32-d839762b3436"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Folder already exists!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataFolder(\"Test\",0.15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBiwyfsySRX6",
        "outputId": "4f962578-78d8-4449-9b4f-1dee8d1c4561"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Folder already exists!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataFolder(\"Validation\",0.15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EItPC7F0Sb5K",
        "outputId": "df407213-12a4-43ed-df03-c12519f69980"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Folder already exists!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_of_images = {}\n",
        "\n",
        "\n",
        "for dir in os.listdir(root_dir):\n",
        "  no_of_images[dir] = len(os.listdir(os.path.join(root_dir, dir)))\n",
        "\n",
        "no_of_images.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtt59I77S0Ai",
        "outputId": "3b9b8548-62ef-4a38-9ff6-362a9ba4ac85"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('no', 737), ('yes', 537)])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Building**\n"
      ],
      "metadata": {
        "id": "ddbggGhATdH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense, BatchNormalization, GlobalAvgPool2D\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import keras"
      ],
      "metadata": {
        "id": "hCWKv8HnTIhL"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# CNN Model\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters= 16, kernel_size= (3,3), activation= 'relu', input_shape= (224,224,3)))\n",
        "\n",
        "model.add(Conv2D(filters= 36, kernel_size= (3,3), activation= 'relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters= 64, kernel_size= (3,3), activation= 'relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters= 128, kernel_size= (3,3), activation= 'relu' ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Dropout(rate= 0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=64, activation= 'relu'))\n",
        "model.add(Dropout(rate= 0.25))\n",
        "model.add(Dense(units= 1, activation= 'sigmoid'))         # used to add non-linearity in model\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "cjplcKnkTPxE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb83ec8e-c7db-41ca-fad2-c32ccdbbbb9f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4 (Conv2D)           (None, 222, 222, 16)      448       \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 220, 220, 36)      5220      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 110, 110, 36)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 108, 108, 64)      20800     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 54, 54, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 26, 26, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 26, 26, 128)       0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 86528)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                5537856   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5638245 (21.51 MB)\n",
            "Trainable params: 5638245 (21.51 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss= keras.losses.binary_crossentropy, metrics=['accuracy'] )"
      ],
      "metadata": {
        "id": "_x1HxaKSTVjF"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing Data"
      ],
      "metadata": {
        "id": "kiH7GYx3UKy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessingImages1(path):\n",
        "  \"\"\"\n",
        "  input : Path\n",
        "  output : Pre processed images\n",
        "  \"\"\"\n",
        "  image_data = ImageDataGenerator(zoom_range=0.2, shear_range= 0.2, rescale= 1/255, horizontal_flip= True)\n",
        "  image = image_data.flow_from_directory(directory = path, target_size=(224,224), batch_size= 32, class_mode= 'binary')\n",
        "\n",
        "  return image"
      ],
      "metadata": {
        "id": "li_6Za0dT8kT"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/gdrive/MyDrive/Brain_Tumor_Prediction/Train\"\n",
        "train_data = preprocessingImages1(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aufDXdYdUP_i",
        "outputId": "e835222f-b64e-4a57-d037-ad6ba07c4d0d"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 152 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def preprocessingImages2(path):\n",
        "  \"\"\"\n",
        "  input : Path\n",
        "  output : Pre processed images\n",
        "  \"\"\"\n",
        "  image_data = ImageDataGenerator(rescale= 1/255)\n",
        "  image = image_data.flow_from_directory(directory = path, target_size=(224,224), batch_size= 32, class_mode= 'binary')\n",
        "\n",
        "  return image\n"
      ],
      "metadata": {
        "id": "YPS-6zyBUUaC"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/gdrive/MyDrive/Brain_Tumor_Prediction/Test\"\n",
        "test_data = preprocessingImages2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7V0VhxdUhCK",
        "outputId": "93ba1677-d258-413a-b065-33f974425677"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 207 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "path = \"/content/gdrive/MyDrive/Brain_Tumor_Prediction/Validation\"\n",
        "val_data = preprocessingImages2(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9qGfFAfUmIq",
        "outputId": "70584de3-aeb5-412b-f7da-e4feda36218c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 387 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "# early stopping\n",
        "es = EarlyStopping(monitor= 'val_accuracy', min_delta= 0.01, patience= 6, verbose= 1, mode= 'auto')\n",
        "# model check point\n",
        "mc = ModelCheckpoint(monitor= 'val_accuracy', filepath=\"/content/gdrive/MyDrive/Brain Tumor Prediction/bestmodel.h5\", verbose= 1, save_best_only= True, mode= 'auto')\n",
        "\n",
        "cd = [es, mc]"
      ],
      "metadata": {
        "id": "NkXrr477Uqgq"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "SpzqNyzgUxUx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hs = model.fit_generator(generator= train_data,\n",
        "                         steps_per_epoch= 10, epochs= 20,\n",
        "                         verbose= 1, validation_data= val_data, validation_steps= 210, callbacks= cd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taNFG2g4UuYK",
        "outputId": "92b7620d-6b8f-4c42-84dd-76a2eaf89b0b"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            " 5/10 [==============>...............] - ETA: 24s - loss: 0.6270 - accuracy: 0.7171"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 200 batches). You may need to use the repeat() function when building your dataset.\n",
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 210 batches). You may need to use the repeat() function when building your dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 1: val_accuracy improved from 0.83204 to 0.86563, saving model to /content/gdrive/MyDrive/Brain Tumor Prediction/bestmodel.h5\n",
            "10/10 [==============================] - 46s 4s/step - loss: 0.6270 - accuracy: 0.7171 - val_loss: 0.3954 - val_accuracy: 0.8656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h = hs.history\n",
        "h.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ga2LzTcDU4sy",
        "outputId": "c933bc82-4d11-4668-e64c-6d2bc458ac54"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(h['accuracy'])\n",
        "plt.plot(h['loss'], c = \"red\")\n",
        "plt.title(\"accuracy vs loss\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "1HaBj3s8yHno",
        "outputId": "656c8157-a923-4c4f-8c2d-f9e12f77b67f"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "model = load_model(\"/content/gdrive/MyDrive/Brain Tumor Prediction/bestmodel.h5\")"
      ],
      "metadata": {
        "id": "InSWJvS5yNDO"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc = model.evaluate_generator(test_data)[1]\n",
        "print(f\"Accuracy {acc}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKEz54WryQ9m",
        "outputId": "71a7e8d5-d410-4bbf-a0ce-d1da80042cc1"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.8115941882133484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install keras_preprocessing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rt6eRaJlyUbu",
        "outputId": "ce54f38c-d7ee-40ee-a5df-3cc34ca9eeb7"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting keras_preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras_preprocessing) (1.25.2)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from keras_preprocessing) (1.16.0)\n",
            "Installing collected packages: keras_preprocessing\n",
            "Successfully installed keras_preprocessing-1.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras_preprocessing.image import load_img, img_to_array"
      ],
      "metadata": {
        "id": "gqO36SlQyaU2"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make Predictions"
      ],
      "metadata": {
        "id": "aTb2PGV12NhG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/gdrive/MyDrive/Brain_Tumor_Prediction/Dataset/no/no_1236.jpg\"\n",
        "img_yes = load_img(path, target_size=(224,224))\n",
        "img_array_yes = img_to_array(img_yes)/255\n",
        "\n",
        "plt.imshow(img_array_yes)\n",
        "plt.show()\n",
        "\n",
        "img_array_yes = np.expand_dims(img_array_yes, axis=0)\n",
        "prediction1 = model.predict(img_array_yes)[0][0]\n",
        "class1 = np.round(prediction1).astype(int)\n",
        "print(class1)\n",
        "if class1 == 0:\n",
        "  print(\"The MRI image doesn't have a Tumor\")\n",
        "else:\n",
        "  print(\"The MRI image has a tumor\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "1EKLiJwyzCQu",
        "outputId": "7a91e1e8-8d48-44bd-8d24-c01daaf2b7b0"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 253ms/step\n",
            "0\n",
            "The MRI image doesn't have a Tumor\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}